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A/B test data cleaning

A/B testing data cleaning is no different from the usual process taught in data cleaning and manipulation courses. However, understanding the context and data logging behavior of each A/B test on a case-by-case basis enables you to make decisions about how to handle messy data. Dropping or keeping duplicates or missing values are two cases where we need to be careful with our metric definitions and how consistent these are across variants.

AdSmart and homepage DataFrames as well as pandas and numpy libraries are loaded for you.

The Adsmart Kaggle dataset source is linked here.

This exercise is part of the course

A/B Testing in Python

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Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Check for duplicate rows due to logging issues 
print(____(AdSmart))
print(____(AdSmart.____(keep='____')))
Edit and Run Code